Gastos Casa
07 January, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 4/11/2024 | Comida | 64915 | Tami | Supermercado |
| 7/11/2024 | Comida | 34229 | Andrés | piwen |
| 8/11/2024 | Assistcard viaje | 44836 | Tami | Assistcard viaje |
| 9/11/2024 | Comida | 35916 | Tami | Supermercado |
| 15/11/2024 | Comida | 12576 | Tami | Supermercado |
| 17/11/2024 | Comida | 91203 | Tami | Supermercado |
| 18/11/2024 | Diosi | 49500 | Tami | Veterinaria |
| 23/11/2024 | Comida | 50929 | Tami | Supermercado |
| 26/11/2024 | Enceres | 12990 | Tami | Confort 48 rollos |
| 27/11/2024 | Transporte | 21780 | Tami | Transvip aeropuerto |
| 27/11/2024 | Electricidad | 45000 | Andrés | Eléctrico |
| 30/11/2024 | Comida | 62899 | Tami | Supermercado |
| 11/12/2024 | Comida | 17738 | Andrés | compra lider despues viaje |
| 12/12/2024 | Comida | 55616 | Tami | Supermercado |
| 14/12/2024 | Diosi | 16276 | Andrés | antiparasitario |
| 15/12/2024 | Comida | 23773 | Tami | Supermercado |
| 18/12/2024 | Comida | 41554 | Tami | Supermercado |
| 18/12/2024 | VTR | 22000 | Andrés | NA |
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 10/12/2024 | Otros | 47484 | Andrés | viaje brasil |
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 30/12/2024 | Comida | 30000 | Andrés | nueces almendras mix etc |
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.9042e+08 2 4.9472 0.0073 **
## lag_depvar 2.6087e+11 1 2606.1724 <2e-16 ***
## Residuals 7.9778e+10 797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1990.265 16447.94 0.1569436
## 2-0 31579.621 23271.349 39887.89 0.0000000
## 2-1 24350.783 19527.658 29173.91 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
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## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
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## 310 37898.00 2 46423.71
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## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
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## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
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## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
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## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 645 53813.88 22609.703
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2026.07916 4043.45423 -541.09651 2435.44948 -2975.61280 516.57210
## 8 9 10 11 12 13
## -5659.04469 -1184.08874 -3962.02793 -409.96968 -4932.84644 -1597.74175
## 14 15 16 17 18 19
## -888.14295 388.14154 -3234.65294 -367.20046 -2120.88701 6614.28656
## 20 21 22 23 24 25
## -1529.56463 -1207.34385 1477.31238 -1187.69221 234.54269 1693.95415
## 26 27 28 29 30 31
## -7105.77540 952.83956 8195.27240 409.74456 -22.44342 -2408.47937
## 32 33 34 35 36 37
## 1571.83571 4566.14277 1114.99832 2379.08437 -1882.35968 4597.26049
## 38 39 40 41 42 43
## 4297.46605 -2286.43201 -2987.91631 -1112.02133 -10741.70847 7302.82750
## 44 45 46 47 48 49
## 2559.61946 1365.57934 8102.25890 673.19754 6516.52995 6695.32337
## 50 51 52 53 54 55
## -5907.61733 -4810.01604 -5066.34474 -7927.89036 6140.53099 -4075.15529
## 56 57 58 59 60 61
## -4887.88349 3867.64874 893.16212 -28.66824 145.21190 -4994.06953
## 62 63 64 65 66 67
## 18135.19299 3624.71999 -3664.72352 5913.78375 7326.36569 14614.09993
## 68 69 70 71 72 73
## 1652.89624 -13249.99420 -1321.60602 4631.76492 -4916.45928 -4412.27994
## 74 75 76 77 78 79
## -10498.43131 2479.81660 -5391.45539 1078.20891 -6854.91693 565.93319
## 80 81 82 83 84 85
## -2338.57399 -2676.69788 -3913.23578 -515.95664 2334.24574 3776.80031
## 86 87 88 89 90 91
## 484.22288 -478.86708 202.12885 4306.42115 -1164.84071 1150.72496
## 92 93 94 95 96 97
## -2066.15226 -1043.08668 179.97236 276.59090 -7482.86824 2402.73249
## 98 99 100 101 102 103
## -8596.04481 -2923.33703 -4020.25767 -1714.29536 -1239.16567 3202.22389
## 104 105 106 107 108 109
## -2327.64821 2609.78693 -1148.08910 981.03443 2595.02900 -3150.94004
## 110 111 112 113 114 115
## -4715.81784 -837.57725 1915.78856 11701.73210 -1251.12859 2662.71963
## 116 117 118 119 120 121
## 4254.20575 3489.46689 -1116.11987 -4728.93941 -3728.73786 2320.76937
## 122 123 124 125 126 127
## -1734.82022 1340.90665 8856.92829 834.15348 118.05106 -2532.22450
## 128 129 130 131 132 133
## 2648.91188 7043.63932 995.29494 -8515.70309 1746.33857 4130.68592
## 134 135 136 137 138 139
## -3173.69129 -1423.99892 -855.78955 -3880.42608 1187.90314 -492.79176
## 140 141 142 143 144 145
## -2910.57563 1724.73018 -1877.63561 -7823.76853 2054.79247 -3469.05878
## 146 147 148 149 150 151
## 2116.17056 -248.17198 1031.43640 -353.39433 1357.77241 1189.62771
## 152 153 154 155 156 157
## 3357.54063 -4865.50732 -1171.23304 -3231.43430 5964.85716 9745.71759
## 158 159 160 161 162 163
## -3668.32388 -5011.98021 3374.68156 -42.20839 2456.97266 -6155.36999
## 164 165 166 167 168 169
## -6985.13074 3926.56297 17153.12484 3353.87759 -677.66166 -2722.61737
## 170 171 172 173 174 175
## -1375.02839 3322.32189 -502.26161 -8348.03107 2605.86851 4061.56887
## 176 177 178 179 180 181
## 353.63723 8477.95276 -9535.99527 -3740.48244 -11007.14913 -11484.41840
## 182 183 184 185 186 187
## 1007.46106 9058.60335 -1685.75825 5673.49116 6286.44917 12874.70892
## 188 189 190 191 192 193
## 8119.75439 -4390.46409 2147.61753 10047.40742 -1984.90607 -2778.71900
## 194 195 196 197 198 199
## -10606.34616 -6664.69958 946.32734 -5523.38703 -10074.74020 5125.10805
## 200 201 202 203 204 205
## -3340.33232 -1978.97188 -1068.71642 6229.36630 9598.84787 269.64854
## 206 207 208 209 210 211
## 2615.13750 2782.65985 5463.74703 12502.32533 -6044.21812 -11633.08799
## 212 213 214 215 216 217
## -5972.34087 -10878.17616 -5340.90460 1271.12714 -13272.27338 16155.47876
## 218 219 220 221 222 223
## 7531.74706 1230.23202 26386.85283 12160.23037 6944.90121 13628.07460
## 224 225 226 227 228 229
## -4335.62740 -2140.41987 3393.36222 -22.67676 2373.51230 8636.09544
## 230 231 232 233 234 235
## 5452.28739 -2284.29219 -2189.61707 9077.89557 -11870.47265 -7610.29567
## 236 237 238 239 240 241
## -8846.78299 -10385.39917 2816.66494 1081.51719 -8572.22665 -9246.30169
## 242 243 244 245 246 247
## 8856.05024 -8030.59053 2238.13319 -10560.34779 -4291.76504 1188.98168
## 248 249 250 251 252 253
## 759.70714 -12566.96756 3417.45620 1820.30947 3958.63610 1866.25342
## 254 255 256 257 258 259
## -1437.79693 10862.25009 20572.24916 2831.93208 -4640.21633 3757.41003
## 260 261 262 263 264 265
## -2053.22443 3388.19531 -5206.25475 -11230.30842 -5033.25615 -813.76915
## 266 267 268 269 270 271
## -5480.26758 8498.27791 -4587.94326 3892.52153 -2417.65457 4125.15251
## 272 273 274 275 276 277
## 389.34650 6981.08395 -1755.29001 11687.57786 -4957.17782 1369.36627
## 278 279 280 281 282 283
## -730.90012 7496.99406 -5433.04942 -3085.70579 -11603.10984 -2971.94213
## 284 285 286 287 288 289
## 18360.33713 7427.58110 2356.26874 -1010.06587 532.26588 6026.53968
## 290 291 292 293 294 295
## 6494.53021 -19176.44661 -11469.27680 -8409.25229 9405.61703 2776.68419
## 296 297 298 299 300 301
## -1485.25508 27100.53224 9666.56933 4474.76069 9085.90183 2403.00694
## 302 303 304 305 306 307
## -1481.32435 7465.93237 -24741.13288 -3874.79029 -495.75211 -7283.52133
## 308 309 310 311 312 313
## -4256.93743 2663.49970 -9472.05209 -3473.69492 -8419.05799 1361.48944
## 314 315 316 317 318 319
## -3367.02047 1839.11301 -4306.54741 27231.08479 -1073.03024 2948.26818
## 320 321 322 323 324 325
## 10477.46775 5197.77084 31975.84588 4591.04838 -21451.68711 1385.61079
## 326 327 328 329 330 331
## 709.50322 -6857.99680 -2089.47029 -33607.64394 719.54154 -2471.54184
## 332 333 334 335 336 337
## -256.14922 -3334.84890 3927.22286 -618.93171 -7136.76780 -3275.02865
## 338 339 340 341 342 343
## -2343.02557 -7828.49265 3728.30629 -1522.11825 -1890.86927 -1147.21498
## 344 345 346 347 348 349
## 19.82079 316.36889 -1793.21389 -9620.99483 -13350.27305 2217.30054
## 350 351 352 353 354 355
## -4440.92803 -3769.15304 -6087.03263 1656.99886 1267.32527 2615.92743
## 356 357 358 359 360 361
## -3928.94320 -671.93364 513.48588 6836.85149 58.43143 -262.30119
## 362 363 364 365 366 367
## 2355.38021 -2993.12855 -1107.82537 -8970.78837 -4811.58919 -6379.68571
## 368 369 370 371 372 373
## -5092.54451 -7380.03948 4912.21043 229.02216 6964.89928 -7836.01144
## 374 375 376 377 378 379
## -2429.51067 -3550.09911 -2619.59276 -12605.80229 1806.90100 -10753.74157
## 380 381 382 383 384 385
## 5616.40920 9210.92619 2940.56283 -2607.96493 1401.81659 6527.06328
## 386 387 388 389 390 391
## 11154.12052 -6120.10464 -5651.56918 -420.23866 8299.41272 1505.47735
## 392 393 394 395 396 397
## 10904.71344 -10251.25355 2456.73704 383.41014 233.24972 -981.80898
## 398 399 400 401 402 403
## -883.65156 -14801.23535 8292.99508 -1452.69052 -1634.70611 6728.96244
## 404 405 406 407 408 409
## -8221.22414 -1538.57962 -2764.03874 -6036.21233 -3044.07893 -4089.06685
## 410 411 412 413 414 415
## -8910.23331 6019.05107 1481.76741 -7548.95973 -7834.32319 14111.19196
## 416 417 418 419 420 421
## 3613.57070 4259.83666 -8297.82187 -4962.09396 -2794.33376 2637.84641
## 422 423 424 425 426 427
## -14212.80980 -2920.54652 -9221.68093 2929.27303 6862.39732 6409.41449
## 428 429 430 431 432 433
## -4198.31643 -4313.50331 -4896.77536 -1944.27400 -5864.39863 -6756.41362
## 434 435 436 437 438 439
## -6054.08755 -1478.72584 -940.58996 -5077.67722 2492.11241 4720.94398
## 440 441 442 443 444 445
## -5213.92499 -2296.84189 1439.42034 -3992.56987 2692.41767 -6745.76854
## 446 447 448 449 450 451
## -12250.01139 -4596.53133 9569.44394 -2173.10426 4615.37623 -6042.00949
## 452 453 454 455 456 457
## -1269.76224 235.07360 2868.13080 -12448.83260 3247.12200 -6850.59726
## 458 459 460 461 462 463
## 6399.64899 2846.25412 2319.68017 -4049.74713 1906.50042 -207.91697
## 464 465 466 467 468 469
## 1590.17517 -735.25255 3138.07937 -2869.72205 5589.53502 -7187.03860
## 470 471 472 473 474 475
## -3169.43961 -2394.49649 -4842.44453 2839.48089 7620.36844 -6236.84849
## 476 477 478 479 480 481
## 1296.41736 -6375.69965 -3010.56423 1856.59293 -13100.19042 -9865.42865
## 482 483 484 485 486 487
## -1275.00384 -60.66578 -1056.64120 -1443.66986 -9691.32735 11024.28097
## 488 489 490 491 492 493
## 6095.30930 7242.00905 -5655.26537 5175.95409 9072.91604 5788.33206
## 494 495 496 497 498 499
## -13763.87869 -10781.06631 -3602.40499 -1253.70796 -671.79637 -7775.99680
## 500 501 502 503 504 505
## 493.81287 4159.56457 5352.48586 472.73802 -111.68126 -7432.44929
## 506 507 508 509 510 511
## 411.32763 -5213.56943 1688.59409 -1454.69448 -8313.90783 -723.15102
## 512 513 514 515 516 517
## -2800.62116 -708.52238 1206.08306 -9635.41884 -7866.39425 24211.71474
## 518 519 520 521 522 523
## 9619.98018 5626.00669 -5613.28536 2553.34777 16764.09365 11140.11062
## 524 525 526 527 528 529
## -24526.24123 -5302.74609 -3947.57035 4377.21864 -575.01322 -11318.14701
## 530 531 532 533 534 535
## 4231.02501 13723.53251 -5232.83538 4146.00443 5307.21829 -2062.07478
## 536 537 538 539 540 541
## -4797.93348 -7303.78395 -2292.68225 8140.19762 -97.31082 -8364.41299
## 542 543 544 545 546 547
## 1634.87154 -791.29460 176.64438 -11223.32589 -11201.33691 1948.42285
## 548 549 550 551 552 553
## 6890.30420 -1473.69779 682.56745 -7883.88926 8435.19458 728.17144
## 554 555 556 557 558 559
## -12129.83836 9032.11386 8474.70106 -129.58098 4625.21900 -3824.68904
## 560 561 562 563 564 565
## 13879.01897 21201.56570 -6861.35181 -10026.25400 6490.23571 -84.25270
## 566 567 568 569 570 571
## 3153.23169 -7686.01536 -17569.37299 6494.27330 6232.75555 1670.79272
## 572 573 574 575 576 577
## 2861.84987 1523.22574 -2414.57619 14483.83877 -9950.28304 -6489.88966
## 578 579 580 581 582 583
## 8500.63380 2606.85866 -6805.60085 7286.73945 -4055.56802 -3006.88243
## 584 585 586 587 588 589
## 15488.54589 -14787.27385 8218.28058 -178.41689 -6455.71534 -961.87032
## 590 591 592 593 594 595
## 50.24297 -10854.44489 1648.40816 -7304.97643 2938.82829 8717.11816
## 596 597 598 599 600 601
## -7696.32854 5691.77665 2544.07135 6651.64794 -3425.77639 5933.67145
## 602 603 604 605 606 607
## -8540.72057 2058.17201 1063.45476 2927.98890 1272.75541 171.94753
## 608 609 610 611 612 613
## -6033.40616 7883.65034 -1411.59033 -2791.14904 -3653.76503 -8409.56725
## 614 615 616 617 618 619
## 11816.75870 4735.64984 -9535.57329 11453.24396 5823.01223 -5822.11655
## 620 621 622 623 624 625
## 26144.94493 -13153.76850 -7030.46781 2947.01501 -4378.84727 -10787.28607
## 626 627 628 629 630 631
## 11152.20043 -21832.81324 -2513.76801 8579.54050 10993.85542 -1746.28232
## 632 633 634 635 636 637
## 33100.09008 -6915.37980 5435.50601 5105.05791 -2572.20905 -5623.54563
## 638 639 640 641 642 643
## -2182.46567 -12656.38120 -2407.26629 -2041.48240 -2668.48024 -2997.12564
## 644 645 646 647 648 649
## 1685.42345 4289.45129 16801.41273 18315.47180 572.11999 4488.53544
## 650 651 652 653 654 655
## 10304.42380 19812.91292 343.97084 -28438.03172 -1571.43442 -2505.07252
## 656 657 658 659 660 661
## 1673.19191 -3387.36149 -10796.76335 1537.22837 4091.30391 -1158.98937
## 662 663 664 665 666 667
## 12885.41019 1162.77511 1616.72362 -11888.75216 1232.91583 1036.80132
## 668 669 670 671 672 673
## -5319.34688 -7541.28332 1963.96610 -3825.11801 2571.82707 -3494.09858
## 674 675 676 677 678 679
## -9441.48212 -8381.33220 -3032.56836 116.78919 2779.69813 626.74803
## 680 681 682 683 684 685
## -3922.03123 -1895.95588 -1405.87044 -8331.87218 4581.35383 -2334.71385
## 686 687 688 689 690 691
## -1488.68548 495.80144 10754.15466 9708.44454 10450.08349 -9865.74674
## 692 693 694 695 696 697
## -3713.37666 -3282.83290 5740.42542 -10534.91492 -8023.18938 -8698.62634
## 698 699 700 701 702 703
## -6339.09536 -4792.31044 3034.00882 -4470.22828 -1960.69517 4157.01658
## 704 705 706 707 708 709
## 31020.56712 9359.50153 23276.44385 1484.40434 8142.61160 22742.25989
## 710 711 712 713 714 715
## 6363.04230 -18389.80439 4686.45801 -5576.61647 -216.02568 370.16098
## 716 717 718 719 720 721
## -17372.33054 -5338.71118 3268.59265 -3086.01242 -13046.35560 4231.63337
## 722 723 724 725 726 727
## -5614.56015 690.79778 -3990.60398 -12499.43612 1332.25754 -1909.79730
## 728 729 730 731 732 733
## -9821.57781 17236.35271 1709.38909 -2792.79879 5648.77176 -8707.29291
## 734 735 736 737 738 739
## -786.14417 8075.09678 -15429.31164 -5963.99557 7361.62512 -4846.77876
## 740 741 742 743 744 745
## 104.37211 1768.41284 -2020.30642 -5230.91183 6356.29699 -6344.12816
## 746 747 748 749 750 751
## 22638.70145 7729.53381 -2053.65502 -7387.57426 23331.81200 -4409.62699
## 752 753 754 755 756 757
## 1291.10930 -14524.78592 56032.44237 26767.14210 14919.59114 -10824.12131
## 758 759 760 761 762 763
## 10461.83705 7167.88758 5665.90313 -46528.04342 -16234.77467 925.71610
## 764 765 766 767 768 769
## -2561.27374 -3499.56735 122805.24468 19123.13305 43528.96325 22363.15765
## 770 771 772 773 774 775
## 11851.16548 15631.74313 25515.53390 -99029.15955 -6840.29541 -35902.56470
## 776 777 778 779 780 781
## 1710.73878 -1260.05305 3364.11241 -7452.65813 -1475.45923 -1976.15426
## 782 783 784 785 786 787
## 3393.78211 -7191.71028 -2266.13799 3890.62562 2230.07758 -2797.91555
## 788 789 790 791 792 793
## -4083.52383 1706.64046 2817.90825 -90.94435 -6743.08194 -5814.74596
## 794 795 796 797 798 799
## -1174.27826 -1290.82006 -7845.49406 -2375.17256 -3289.63232 -2686.15659
## 800 801 802
## 10703.78980 2205.52327 7040.57832
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17243.21 20095.55 24357.24 24074.69 26432.33 23760.14 24477.76 19701.23
## 10 11 12 13 14 15 16 17
## 19437.31 16775.26 17554.13 14277.60 14328.86 14994.72 16694.37 15011.34
## 18 19 20 21 22 23 24 25
## 16047.89 15420.28 22515.56 21597.92 21076.83 22970.26 22295.03 22948.76
## 26 27 28 29 30 31 32 33
## 24798.06 18715.45 20444.73 28296.26 28354.01 28026.34 25651.45 27056.43
## 34 35 36 37 38 39 40 41
## 30906.43 31255.49 32667.22 30173.31 34145.53 37359.43 34410.20 31215.31
## 42 43 44 45 46 47 48 49
## 30060.99 20623.46 28155.81 30596.71 31687.88 38538.37 38032.04 42702.68
## 50 51 52 53 54 55 56 57
## 46946.62 39631.30 34189.92 29203.60 22335.61 28637.01 25211.45 21502.35
## 58 59 60 61 62 63 64 65
## 25918.70 27180.53 27478.07 27890.64 23754.09 40375.42 42222.72 37460.07
## 66 67 68 69 70 71 72 73
## 41674.63 46599.19 57286.68 55296.85 40513.32 38014.66 41038.03 35327.85
## 74 75 76 77 78 79 80 81
## 30771.86 21458.47 24665.74 20584.08 22673.92 17560.21 19579.29 18804.41
## 82 83 84 85 86 87 88 89
## 17830.38 15895.81 17175.90 20790.49 25216.21 26207.87 26232.87 26850.72
## 90 91 92 93 94 95 96 97
## 30983.27 29811.70 30812.87 28873.80 28072.17 28440.98 28848.30 22414.12
## 98 99 100 101 102 103 104 105
## 25434.62 18452.48 17306.54 15343.72 15644.02 16322.63 20803.36 19885.21
## 106 107 108 109 110 111 112 113
## 23402.66 23192.25 24871.40 27753.37 25246.96 21684.01 21959.93 24610.98
## 114 115 116 117 118 119 120 121
## 35495.13 33684.71 35525.51 38529.25 40488.69 38172.94 32984.60 29319.37
## 122 123 124 125 126 127 128 129
## 31405.96 29682.81 30866.50 38479.99 38121.81 37181.65 34039.52 35823.93
## 130 131 132 133 134 135 136 137
## 41231.56 40670.85 31856.66 33123.74 36319.26 32723.43 31107.79 30191.14
## 138 139 140 141 142 143 144 145
## 26741.95 28158.93 27928.15 25610.27 27638.35 26260.63 19851.21 22887.20
## 146 147 148 149 150 151 152 153
## 20709.97 23692.46 24233.42 25826.68 26009.08 27666.23 28969.32 32006.94
## 154 155 156 157 158 159 160 161
## 27468.95 26730.58 24281.43 30186.14 41688.75 40015.98 37376.18 42405.49
## 162 163 164 165 166 167 168 169
## 43816.60 47238.66 42696.42 37995.15 43430.16 59761.69 61977.80 60389.05
## 170 171 172 173 174 175 176 177
## 57209.03 55605.39 58312.83 57335.17 49613.42 52442.00 56191.36 56227.62
## 178 179 180 181 182 183 184 185
## 63369.28 53854.48 50599.58 41391.70 32915.82 36430.40 46552.04 46007.08
## 186 187 188 189 190 191 192 193
## 51970.55 57725.86 68528.25 73820.61 67503.95 67697.74 74780.76 70449.43
## 194 195 196 197 198 199 200 201
## 65964.20 55188.70 49208.10 50634.96 46221.74 38376.46 44812.76 43036.97
## 202 203 204 205 206 207 208 209
## 42674.29 43153.49 49959.72 58864.92 58493.86 60221.77 61880.54 65678.53
## 210 211 212 213 214 215 216 217
## 75162.08 67230.66 55398.48 49997.60 40977.76 37930.02 41049.27 31051.52
## 218 219 220 221 222 223 224 225
## 48055.54 55389.48 56293.00 79099.34 86607.81 88614.64 96219.63 87154.28
## 226 227 228 229 230 231 232 233
## 81141.92 80723.11 77367.06 76527.05 81272.57 82639.29 77064.76 72269.10
## 234 235 236 237 238 239 240 241
## 77932.90 64556.72 56578.93 48515.11 40111.62 44311.05 46467.66 39906.59
## 242 243 244 245 246 247 248 249
## 33574.81 43875.73 38112.30 42055.06 34305.05 33008.59 36670.44 39499.40
## 250 251 252 253 254 255 256 257
## 30312.40 36261.12 40069.36 45273.46 47996.65 47488.32 57807.75 75336.35
## 258 259 260 261 262 263 264 265
## 75151.07 68449.73 69934.22 66148.23 67596.97 61343.45 50598.83 46619.05
## 266 267 268 269 270 271 272 273
## 46828.84 42928.58 51748.51 48014.91 52169.08 50282.28 54356.94 54653.49
## 274 275 276 277 278 279 280 281
## 60681.72 58311.71 68002.03 61915.92 62126.33 60472.43 66225.62 59944.85
## 282 283 284 285 286 287 288 289
## 56502.54 46036.08 44429.95 61693.13 67233.16 67643.35 65056.31 64142.03
## 290 291 292 293 294 295 296 297
## 68150.18 72067.45 53029.85 43114.11 37114.38 47454.32 50701.97 49814.32
## 298 299 300 301 302 303 304 305
## 74054.14 80010.24 80679.10 85299.85 83495.18 78516.50 81989.56 56843.22
## 306 307 308 309 310 311 312 313
## 53097.61 52776.81 46555.79 43760.21 47370.05 39908.84 38628.63 33180.37
## 314 315 316 317 318 319 320 321
## 36971.73 36151.60 39989.98 37970.77 63803.60 61640.87 63267.39 71279.94
## 322 323 324 325 326 327 328 329
## 73671.58 99199.24 97573.97 73360.53 72156.21 70510.57 62447.76 59564.79
## 330 331 332 333 334 335 336 337
## 29458.89 33153.11 33593.43 35917.56 35257.21 41034.65 42112.20 37351.17
## 338 339 340 341 342 343 344 345
## 36564.17 36691.06 32001.55 38011.40 38676.01 38934.93 39812.32 41601.49
## 346 347 348 349 350 351 352 353
## 43426.79 43177.99 36109.84 26660.56 32014.93 30873.87 30463.18 28075.29
## 354 355 356 357 358 359 360 361
## 32762.67 36523.79 40995.51 39181.22 40443.80 42586.15 49994.85 50546.44
## 362 363 364 365 366 367 368 369
## 50748.48 53216.13 50694.97 50138.50 42770.30 39961.97 36131.97 33906.61
## 370 371 372 373 374 375 376 377
## 29957.22 37258.41 39549.53 47449.44 41410.08 40856.24 39390.88 38922.80
## 378 379 380 381 382 383 384 385
## 29773.81 34380.31 27419.31 35653.65 46005.58 49577.54 47847.75 49843.08
## 386 387 388 389 390 391 392 393
## 56074.59 65577.39 58776.28 53234.38 52962.59 60355.67 60880.00 69564.54
## 394 395 396 397 398 399 400 401
## 58650.26 60220.02 59779.32 59262.24 57746.37 56505.66 43240.00 51841.40
## 402 403 404 405 406 407 408 409
## 50839.99 49804.32 56217.37 48746.15 48056.04 46379.64 42048.94 40877.50
## 410 411 412 413 414 415 416 417
## 38937.80 33021.09 40908.38 43840.10 38502.61 33581.81 48480.86 52332.73
## 418 419 420 421 422 423 424 425
## 56269.25 48724.52 45041.05 43714.58 47307.67 35705.40 35434.11 29682.30
## 426 427 428 429 430 431 432 433
## 35282.46 43625.44 50530.32 47289.79 44353.06 41272.56 41160.54 37631.84
## 434 435 436 437 438 439 440 441
## 33763.09 30992.01 32571.02 34423.82 32424.74 37299.91 43516.92 40263.27
## 442 443 444 445 446 447 448 449
## 39968.72 42980.71 40862.87 44859.77 40097.87 31113.53 29948.84 41326.82
## 450 451 452 453 454 455 456 457
## 41007.77 46669.44 42297.48 42647.78 44271.30 47996.40 37851.88 42710.17
## 458 459 460 461 462 463 464 465
## 38124.92 45708.03 49234.61 51860.03 48583.50 50928.63 51130.54 52880.82
## 466 467 468 469 470 471 472 473
## 52377.49 55326.72 52650.04 57710.61 50958.01 48564.50 47148.02 43766.09
## 474 475 476 477 478 479 480 481
## 47529.20 55006.42 49423.01 51129.41 45908.56 44284.55 47122.76 36517.29
## 482 483 484 485 486 487 488 489
## 30066.86 31939.67 34641.36 36134.10 37101.76 30730.72 43284.26 49956.85
## 490 491 492 493 494 495 496 497
## 56799.84 51501.47 56343.51 63991.38 67809.88 54040.64 44600.98 42622.28
## 498 499 500 501 502 503 504 505
## 42946.08 43738.71 38215.19 40618.58 45929.94 51622.12 52333.11 52443.88
## 506 507 508 509 510 511 512 513
## 46134.10 47476.57 43728.83 46489.41 46154.48 39858.58 40991.76 40165.38
## 514 515 516 517 518 519 520 521
## 41273.06 43917.99 36744.82 32015.43 55949.45 64125.28 67785.00 61151.80
## 522 523 524 525 526 527 528 529
## 62493.76 76104.60 83094.24 57998.03 52858.57 49546.78 53933.87 53439.29
## 530 531 532 533 534 535 536 537
## 43604.69 48605.75 61289.69 55800.42 59204.35 63199.50 60246.65 55268.21
## 538 539 540 541 542 543 544 545
## 48718.40 47371.80 55323.60 55073.56 47619.84 49847.58 49673.93 50369.04
## 546 547 548 549 550 551 552 553
## 41000.77 32821.43 37171.27 45302.84 45099.43 46808.46 40807.23 49836.83
## 554 555 556 557 558 559 560 561
## 50994.27 40754.60 50313.16 58190.44 57554.21 61158.55 56917.98 68700.15
## 562 563 564 565 566 567 568 569
## 85419.49 75492.25 64034.76 68462.11 66583.05 67771.87 59326.37 43286.01
## 570 571 572 573 574 575 576 577
## 50307.53 56223.49 57408.44 59487.77 60136.00 57257.16 69526.28 58880.18
## 578 579 580 581 582 583 584 585
## 52591.65 60207.14 61713.89 54795.26 61073.28 56641.31 53680.45 67275.42
## 586 587 588 589 590 591 592 593
## 52677.29 60034.99 59125.72 52836.44 52140.33 52416.87 43115.73 45917.69
## 594 595 596 597 598 599 600 601
## 40534.31 44787.88 53567.19 46886.22 52755.93 55138.07 60817.49 56968.61
## 602 603 604 605 606 607 608 609
## 61791.15 53344.40 55227.83 56005.58 58317.96 58893.05 58432.98 52599.78
## 610 611 612 613 614 615 616 617
## 59674.30 57730.86 54822.77 51522.85 44472.96 56004.21 59898.72 50817.61
## 618 619 620 621 622 623 624 625
## 61238.56 65431.12 58909.06 81177.05 66272.75 58588.13 60594.70 55939.57
## 626 627 628 629 630 631 632 633
## 46257.37 56984.24 37505.20 37365.17 46950.86 57452.57 55493.62 84274.81
## 634 635 636 637 638 639 640 641
## 74443.21 76647.94 78288.21 73004.97 65711.04 62339.24 50222.27 48587.63
## 642 643 644 645 646 647 648 649
## 47477.19 45956.70 44338.43 47020.12 51645.87 66643.81 81094.17 78212.32
## 650 651 652 653 654 655 656 657
## 79117.72 84999.80 98468.74 93217.89 63434.29 60881.50 57830.38 58816.79
## 658 659 660 661 662 663 664 665
## 55251.33 45646.77 48035.41 52360.99 51551.73 63134.37 63011.85 63301.90
## 666 667 668 669 670 671 672 673
## 51736.51 53098.48 54118.78 49449.14 43418.03 46458.40 44052.89 47545.96
## 674 675 676 677 678 679 680 681
## 45294.34 38119.05 32767.43 32764.93 35518.87 40259.39 42523.89 40524.81
## 682 683 684 685 686 687 688 689
## 40548.44 40998.02 35330.22 41671.00 41167.54 41467.34 43466.42 54193.41
## 690 691 692 693 694 695 696 697
## 62665.92 70729.60 60007.23 56007.83 52884.57 58047.91 48323.33 42011.05
## 698 699 700 701 702 703 704 705
## 35895.81 32609.02 31086.28 36602.80 34863.27 35537.13 41480.72 70191.64
## 706 707 708 709 710 711 712 713
## 76361.27 93939.88 90252.53 92852.45 107904.53 106743.09 84064.40 84412.33
## 714 715 716 717 718 719 720 721
## 75735.17 72832.70 70805.62 53504.43 48894.55 52392.87 49893.21 38988.94
## 722 723 724 725 726 727 728 729
## 44566.85 40831.49 43080.60 40952.01 31642.74 35600.51 36226.86 29851.08
## 730 731 732 733 734 735 736 737
## 47950.90 50202.51 48232.94 53896.86 46290.00 46565.05 54560.60 40988.14
## 738 739 740 741 742 743 744 745
## 37393.80 45910.06 42678.91 44184.16 46957.73 46069.34 42482.13 49483.27
## 746 747 748 749 750 751 752 753
## 44495.58 65494.75 70824.37 66926.86 58848.05 78661.77 71723.89 70641.21
## 754 755 756 757 758 759 760 761
## 55852.56 104658.00 121758.41 126355.41 107849.02 110281.54 109527.67 107553.47
## 762 763 764 765 766 767 768 769
## 60148.63 45173.57 47086.13 45708.28 43681.33 152442.15 156886.75 182134.99
## 770 771 772 773 774 775 776 777
## 185707.69 179634.83 177628.75 184522.87 81561.87 72134.71 38450.98 41889.91
## 778 779 780 781 782 783 784 785
## 42299.60 46704.94 41094.03 41414.58 41256.93 45818.42 40546.57 40243.52
## 786 787 788 789 790 791 792 793
## 45366.35 48396.34 46647.81 43992.50 46735.95 50109.37 50515.94 45050.17
## 794 795 796 797 798 799 800 801
## 41079.28 41665.25 42076.07 36699.32 36781.20 36052.59 35943.07 47565.33
## 802
## 50299.28
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8125
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.947232 0.7622038 3.844131
## t2* 2606.172376 172.4718004 884.851602
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.0675 4.925997 13.0776
## 2 lag_depvar 1571.8584 2638.208926 4437.8076Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Tue Jan 07 01:05:26 2025
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | NA | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | NA | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | NA | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | NA | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | NA | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | NA | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | NA | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | NA | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | NA | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | NA | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | NA | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | NA | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 0 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2565, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2025-01-09 00:04:58 sería de: -2.166.675 pesos// Percentil 95% más alto proyectado: -1.468.666
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 27573.51 | 26339.42 |
| Lo.80 | 27722.45 | 26498.50 |
| Point.Forecast | 28003.80 | 26799.01 |
| Hi.80 | 28285.17 | 31986.57 |
| Hi.95 | 28434.12 | 34732.70 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5604 -0.3058
## s.e. 0.1105 0.1108
##
## sigma^2 = 37825: log likelihood = -467.86
## AIC=941.72 AICc=942.09 BIC=948.47paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 xreg
## 0.3591 32.9386
## s.e. 0.0994 0.9052
##
## sigma^2 = 33613: log likelihood = -469.8
## AIC=945.61 AICc=945.97 BIC=952.4forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | -1577.9028 | 723.4786 | 766.4502 |
| Lo.80 | -1445.7461 | 867.6069 | 870.0364 |
| Point.Forecast | -1196.0962 | 1139.8716 | 1105.4076 |
| Hi.80 | -946.4463 | 1431.8466 | 1423.9349 |
| Hi.95 | -814.2897 | 1586.4089 | 1628.1543 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))